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Implement a PySpark job to read CSV data, perform joins, and store output as partitioned Parquet.

Spark/Big Datahard0.4 min readPremium
Frequency
Low
Asked at 1 company
Category
452
questions in Spark/Big Data
Difficulty Split
88E|81M|283H
in this category
Total Bank
1,863
across 7 categories
Asked at these companies
Fragma Data Systems
Key Concepts Tested
joinoptimizationpartitionspark
Expert AnswerPremium
80 wordsInterview-ready
**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams. PySpark CSV join → Parquet: `df1 = spark.read.option('header',True).csv('path1'); df2 = spark.read.csv('path2'); joined = df1.join(df2, 'key'); joined.write.mode('overwrite').partitionBy('date').parquet('output')`. For partition efficiency: `partitionBy` on high-cardinality filter columns....
The complete answer continues with detailed implementation patterns, architectural trade-offs, and production-grade considerations. It covers performance optimization strategies, common pitfalls to avoid, and real-world examples from companies like Fragma Data Systems. The answer also includes follow-up discussion points that interviewers commonly explore.

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